Triple

T36828595
Position Surface form Disambiguated ID Type / Status
Subject Earl Macartney E910074 entity
Predicate nobleTitle P914 FINISHED
Object Viscount Macartney
Viscount Macartney is a British noble title historically associated with the Macartney family, notably borne by the diplomat and colonial administrator George Macartney.
E264547 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Viscount Macartney | Statement: [Earl Macartney, nobleTitle, Viscount Macartney]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Viscount Macartney
Triple: [Earl Macartney, nobleTitle, Viscount Macartney]
Generated description
Viscount Macartney is a British noble title historically associated with the Macartney family, notably borne by the diplomat and colonial administrator George Macartney.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69f76e7e9d60819092442fba73290a46 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7cabbc1008190a77cf3b503edb7df completed May 3, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3dfad43a5481909355e2c2940231dd completed June 26, 2026, 4:06 a.m.
NEDg Description generation batch_6a3dfe74a990819090ea0325e6e3f86c completed June 26, 2026, 4:22 a.m.
NED2 Entity disambiguation (via description) batch_6a3e0675bf388190b85f43e22fbbbd5f completed June 26, 2026, 4:56 a.m.
Created at: May 3, 2026, 4:13 p.m.